Influences of Climate Change on Area Variation of Qinghai Lake

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OPEN Infuences of climate change on area variation of Qinghai Lake on Qinghai-Tibetan Plateau since Received: 8 January 2018 Accepted: 25 April 2018 1980s Published: xx xx xxxx Lingyi Tang1, Xiaofang Duan1, Fanjin Kong2, Fan Zhang1, Yangfan Zheng1, Zhen Li 1,3, Yi Mei4, Yanwen Zhao1 & Shuijin Hu1,5

Qinghai-Tibetan Plateau is the most sensitive region to global warming on Earth. Qinghai Lake, the largest lake on the plateau, has experienced evident area variation during the past several decades. To quantify the area changes of Qinghai Lake, a satellite-based survey based on Landsat images from the 1980s to 2010s has been performed. In addition, meteorological data from all the seven available stations on Qinghai-Tibetan Plateau has been analyzed. Area of Qinghai Lake shrank ~2% during 1987–2005, and then increased ~3% from 2005–2016. Meanwhile, the average annual temperature increased 0.319 °C/10 y in the past 50 years, where the value is 0.415 °C/10 y from 2005–2016. The structural equation modeling (SEM) shows that precipitation is the primary factor infuencing the area of Qinghai Lake. Moreover, temperature might be tightly correlated with precipitation, snow line, and evaporation, thereby indirectly causes alternations of the lake area. This study elucidated the signifcant area variation of water body on the Qinghai-Tibetan Plateau under global warming since 1980s.

Qinghai-Tibetan Plateau is the largest plateau with the highest average altitude on the planet. It is also the most sensitive area on Earth1. In addition, it is highly infuenced by the East Asian monsoon, the Indian monsoon, and the Westerly2–4. Qinghai Lake (N36.51°–37.25°; E99.58°–100.79°) is located in northeastern Qinghai-Tibetan Plateau, and it is the largest lake in China (see Fig. 1). It is a salt lake with the lake area of ~4300 km2. Te lake is surrounded by Qilian Mountains, AltynTagh Mountains, and the Kunlun Mountains. Gangshika peak (N101.51°, E37.69°, 5254.5 m) of Qilian Mountains is ~120 km away from Qinghai Lake, which is the peak nearest to Qinghai Lake with altitude >5000 m. Te annual mean temperature of the Qinghai-Tibetan Plateau has increased by 0.27 °C/10 y during 1961– 20055. Te increasing rate is higher than global mean surface temperature warming, i.e., 0.85 °C since 18806. It was also proposed that over 7000 glaciers in Qinghai-Tibetan Plateau have been shrunk due to global warming7. Fluctuation of discharge of rivers8,9, as well as geologic hazards resulting from glaciers changes10, hence frequently occurs. Previous studies have proposed that lakes are sensitive to global climate change and refect alternations of sur- rounding environment11–13. Te surface area of lakes, including the lakes in Qinghai-Tibetan Plateau, is a reliable indicator of climate change3,14. However, geological resources in many lakes on the Qinghai-Tibetan Plateau have been exploited, which dramatically infuences the shape and area of the lakes15. Moreover, the development of agriculture and reclaiming farming resulted in the shrinking of Qinghai Lake during the 1950s to 1960s3. To the contrast, Qinghai Lake had been managed as Qinghai Lake National Nature Reserve since 1975, which avoids

1College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China. 2MLR Key laboratory of Saline Lake Resources and Environment, Institute of Mineral Resources, Chinese Academy of Geological Science (CAGS), Beijing, 100037, China. 3State Key Laboratory for Mineral Deposits Research, Nanjing University, Nanjing, Jiangsu, 210046, China. 4School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing, 100083, China. 5Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC, 27695, USA. Lingyi Tang and Xiaofang Duan contributed equally to this work. Correspondence and requests for materials should be addressed to Z.L. (email: [email protected]) or Y.M. (email: [email protected]) or Y.Z. (email: [email protected])

SCIENtIfIC Reports | (2018)8:7331 | DOI:10.1038/s41598-018-25683-3 1 www.nature.com/scientificreports/

Figure 1. Te distributions of all the seven meteorological stations (Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu, and Lhasa). Gangshika peak is marked as triangle. Te image was generated by ArcGIS sofware (Version 10.2 for Desktop).

Figure 2. Annual precipitation of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu and Lhasa from 1970– 2016. Te black solid line is the trend line of average values of annual precipitation for the seven locations.

anthropogenic changes of the lakes. Terefore, Qinghai Lake is an ideal candidate to explore the responses of waterbody alternations to climate variations. Te aim of this study is to investigate the correlations among climate change, average altitude of snow line, and the area of Qinghai Lake based on spatial analysis of remotely sensed imagery and meteorological data. Te remote sensing images of Qinghai Lake during 1987–2016 were collected to calculate its area. Meanwhile, the meteorological data of Qinghai-Tibetan Plateau during 1970–2016 was collected and analyzed. Results Climate fuctuation. Between 1970 and 2015, annual precipitation for the seven meteorological stations in Qinghai-Tibetan Plateau increased by 10.7 mm per decade. Compared with temperature, precipitation variation on Qinghai-Tibetan Plateau had evidently spatial heterogeneity. Annual precipitation variedamong the seven stations, e.g., the annual precipitation changes of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu and Lhasa were 3.295, 15.38, 15.603, 1.452, 1.754, −1.269 and 19.271 mm per decade respectively (Fig. 2). Lhasa and Gangcha had the largest increase of annual precipitations among all the meteorological stations. From 2005 to 2016, annual precipitations of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu and Lhasa had the changes of −17.063, −52.598, 81.262, −0.521, −44.395, 13.199 and 70.927 mm/10 y respectively. Te precipitation in Gangcha meteorological station hence had the largest value. Moreover, average annual precipitation in Gangcha during 2005–2016 was 313.53 mm, with an elevation of 26.26 mm than that during 1970–2004. Glaciers are of considerable interest because of their high sensitivity to global warming15. Remote sensing images around August were selected to study the changes from 1987 to 2016. According to Fig. 3, the average altitude of snow line of Gangshika peak declined from 4342.2 to 4326.9 m during 1995–2005. However, from 2006–2016, the snow line was elevated from 4360.2 to 4385.3 m due to snow melting. Evaporation is an important factor related to water budget on Qinghai-Tibetan Plateau, which is afected directly by climate change3. Annual pan evaporation of Dachaidan, Dulan, Gangcha, Golmud, and Xining has decreased by 6.255, 11.059, 1.343, 16.21 and 19.433 mm/y respectively during the period of 1970–2003 (Fig. 4).

SCIENtIfIC Reports | (2018)8:7331 | DOI:10.1038/s41598-018-25683-3 2 www.nature.com/scientificreports/

Figure 3. Area of Qinghai Lake in July and August and average altitude of snow line in Gangshika peak ranged from 1987–2016.

Figure 4. Annual pan evaporation of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu and Lhasa from 1970–2003 measured by small evaporator. Te black solid line is the trend line of the average values of annual pan evaporation collected from the seven locations.

Figure 5. Average daily pan evaporation of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu, and Lhasa from June to August within 2004–2016 (no available data of Yushu in 2006–2007 and 2015–2016). Te evaporation was measured by evaporation tank. Te black solid line is the trend line of the average values of average daily pan evaporation collected from the seven locations.

However, annual pan evaporation of Yushu and Lhasa increased by 9.089 and 1.634 mm/y. Te average value of annual pan evaporation of all stations was −6.34 mm/y. During the period of 2004–2016, average daily evaporation from June to August of the seven locations decreased by 0.5–0.9 mm/10 y, including Yushu and Lhasa (the measuring methods were diferent due to the change of evaporator) (Fig. 5). During the period of 1970–2015, the average value of annual mean temperature in the seven meteorological stations increased 0.319 °C per decade. Furthermore, the temperatures of all stations increased to diferent

SCIENtIfIC Reports | (2018)8:7331 | DOI:10.1038/s41598-018-25683-3 3 www.nature.com/scientificreports/

Figure 6. Annual mean temperature of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu and Lhasa from 1970–2016. Te black solid line is the trend line of average values of annual mean temperature collected from the seven locations.

Year Month Area (m2) 1987 8 4368519000 1992 8 4359278700 1995 8 4345330500 1999 8 4333906800 2001 7 4290741900 2002 7 4284276300 2005 7 4276696500 2006 8 4307135400 2010 7 4358232900 2011 7 4409311500 2013 8 4424501248 2014 7 4397698800 2015 7 4419206100 2016 7 4393734144

Table 1. Area of Qinghai Lake from 1987 to 2016 calculated by remote sensing data. Images in 2013 and 2016 were selected from Gaofen-1 WFV4, other years were based on Landsat TM/ETM+/OLI. Data of the least clouds interference was selected in July or August (obvious alternations of snow line).

Index χ2/df GFI NFI CFI RMSEA Evaluation Criterion <3 >0.9 >0.9 >0.9 <0.1 Result 1.04 0.97 0.92 0.97 0.02

Table 2. Goodness of ft index for SEM model (the model of relationships among Qinghai Lake area, temperature, precipitation, evaporation, and snow line).

extents. Te annual mean temperature changes of Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu, and Lhasa were 0.542, 0.305, 0.371, 0.489, 0.014, 0.358 and 0.514 °C per decade respectively (Fig. 6). Xining station shows the lowest increase of temperature in the past 50 years, probably due to that it is on the edge of the plateau.

Dynamic changes of Qinghai Lake area and SEM results. Remote sensing images in the period of mid-July to mid-August were further selected to study the changes among years from 1987 to 2016. In Table 1, Qinghai Lake’s area has fuctuated from 1987 to 2016. In the period of 1987–2005, Qinghai Lake shrank 91.82 km2 constantly. Ten, it increased by 147.8 km2 from 2005 to 2013 constantly. From 2013 to 2016, the lake area fuctuated with the small variation of ~30 km2. Te rationality test of SEM includes coefcient rationality, signifcance test, and goodness of ft test. Te over- all ftness of the model is expressed by normed ft index (NFI), Goodness of ft index (GFI), Comparative ft index (CFI), Incremental ft index (IFI), and Root mean square error of approximation (RMSEA). Fitting results of the model are given directly by AMOS sofware (Table 2). It indicates the model ftted well, and the relationships among the lake area, temperature, precipitation, evaporation, and snow line can be well refected. Te maximum likelihood method was applied to evaluate the model. As shown in Fig. 7, the lake area is afected by precipitation, snow line, evaporation, and temperature. It confrmed a strong positive correlation

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Figure 7. A model of relationships between Qinghai Lake area, temperature, precipitation, evaporation and snow line. Data of Gangcha station that nearest to Qinghai Lake is selected (Tickness of the line indicates the strength of the correlation, and the number represents the correlation coefcient. Te dotted line indicates that the correlation is not signifcant).

between the precipitation and lake area (coefcient =0.76, P = 0.025). Snow line and lake area are also positively related (coefcient = 0.23, P = 0.036), while both evaporation and temperature have evidently negative correlation with lake area (coefcient = −0.17, P = 0.049; coefcient = −0.03, P = 0.065). Temperature has a certain impact on precipitation, snow line, and evaporation (coefcient = 0.10, P = 0.043; coefcient = 0.16, P = 0.010; coefcient = −0.06, P = 0.020). Moreover, efect of precipitation on the snowline around Qinghai Leke is notable (coefcient = −0.12, P = 0.012), only second to temperature. Furthermore, there is a weakly negative correlation between the evaporation and snow line (coefcient = −0.07, P = 0.030). Discussion Change of lake area is tightly related to climate and environment evolution16,17. Te alternations of the Qinghai Lake area, ideally, mostly refect the natural processes17. Precipitation, evaporation, temperature, and snow line are common climatic factors, which afect the area of Qinghai Lake more directly than other natural factors in a short term17,18, i.e., since 1980s in this study. Climatic characteristics of the Qinghai-Tibetan Plateau are heterogeneous based on seven meteorological stations, excluding temperature. Tis confrms the sensitivity Qinghai-Tibetan Plateau to global warming. Te area of Qinghai Lake had shrunk slowly in the period of 1987 to 2005. In the downward trend of lake area from 1987–2005, precipitation increased slightly, along with lower snow line and quite higher evaporation. Ten, a rapid increase of lake area was observed from 2005–2016. Within this period, it also had increasing precipitation, but with elevated snow line and reduced evaporation. In addition, there is a certain fuctuation of lake area from 2014 to 2016, which is consistent with the precipitation changes (also with evident fuctuation) in Gangcha station (the nearest meteorological station to Qinghai Lake). Terefore, the change of the lake area on the plateau should be attributed to combined climatic variables. As an important response area of global climate change, the temperature was rising in Qinghai-Tibetan Plateau, especially in recent years, which has also been revealed in the previous studies5,19–22. Moreover, although temperature has no signifcantly direct infuence on the area of Qinghai Lake, it drives the changes of snow line, thereby afecting the lake area. Furthermore, evaporation usually contributes to outfow of lakes, but it has the low magnitude on the plateau. It is also possible that the vegetation and wind, in addition to water bodies, could infuence evaporation. Terefore, evaporation has limited efect on the area of Qinghai Lake based on our results. As the SEM model shows that precipitation is the primary factor of Qinghai Lake area variation. It is consistent with the previous study23, which reconstructed the fuctuation of lake level in the last 600 years and the results showed that there was a tight relationship between lake level and precipitation. Rivers, whose runof due to glaciers and precipitation, plays important roles in the supply of natural water of Qinghai Lake. Although surface runof accounts for 46.59% of the water in Qinghai Lake, D-O water isotope experiments had demonstrated that the primary source of runof was precipitation, not glacial meltwater18. Terefore, although evaporation, snow line, and temperature have certain infuences on Qinghai Lake area, the primary driver of the change in Qinghai Lake area is the increase of precipitation. Meanwhile, all the available data of remote sensing images and meteorological information related to the research were collected and analyzed. More available data, including both remote sensing and climatic data, would promote our further understanding of the global climate changes on the Qinghai-Tibetan Plateau. Methods Data collection. Meteorological data of all available bases of China Meteorological Network (http://data. cma.cn/) in Qinghai-Tibetan Plateau was collected, i.e., Dachaidan, Dulan, Gangcha, Golmud, Xining, Yushu, and Lhasa. Te locations of the seven stations are shown in Fig. 1 and Table 3. Annual mean temperature and annual precipitation data were collected from 1970 to 2015 (all the available data). In addition, evaporation data from 1970 to 2016 were also analyzed. From 1970 to 2003, annual pan evaporation was provided. Afer 2003, only daily evaporation was available and diferent evaporator were applied. Tus, average daily evaporation from June to August afer 2003 was selected to match the following GIS data. Landsat TM/ETM+/OLI images covering the studied area from 1987 to 2015 were selected, which were downloaded from USGS website (http://landsat.usgs.gov/). Data of several years cannot be obtained due to cloud interference, which is shown in Table 1. Snow line of Gangshika peak was analyzed by GIS images during July-August, when Qinghai has the highest temperature. Two Gaofen-1 WFV4 images were selected due to the

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Location Province Longitude (N) Latitude (E) Dachaidan Qinghai 37°51′16.98″ 95°21′15.95″ Dulan Qinghai 36°18′14.09″ 98°05′40.51″ Gangcha Qinghai 37°19′40.38″ 100°08′30.22″ Golmud Qinghai 36°24′10.46″ 94°54′08.62″ Xining Qinghai 36°37′02.27″ 101°46′33.24″ Yushu Qinghai 33°00′26.49″ 97°00′22.00″ Lhasa Tibet 29°38′48.50″ 91°06′46.58″

Table 3. Coordinates of the seven available meteorological stations on Qinghai-Tibetan Plateau.

lack of appropriate images of the lake (year 2013 and 2016), which can be gained from RS Cloud Mart (http:// www.rscloudmart.com/en). Additionally, remote sensing images of all months were selected to study the changes of Qinghai Lake for reference and calibration.

Data analysis. EVNI (Version 5.0) and ArcGIS sofware (Version 10.2 for Desktop) were applied to pro- cess the Landsat TM/ETM+/OLI images (L1T) for lake area calculating. Modifcation of the normalized diference water index (MNDWI)24 and reclassifcation were selected to calculate the area of Qinghai Lake by Landsat TM/ETM+/OLI images. Gaofen-1 WFV4 images require pretreatment, including radiometric calibration, ortho-rectifcation, and atmospheric correction. Band ratio (band 2/band 4) was applied to calculate the area via GF-1 WFV4 images. Bands 5, 4, and 3 (for R, G and B respectively) of Landsat TM/ETM+ were selected for false color compos- ites (bands 6, 4, and 3 for Landsat8 OLI), so that snow and glaciers could be diferentiated. More than 20 points were selected along the snow line of Lenglong mountain range, with equal points distributed on the south and north slope. In addition, distances between the two adjacent points on the mountain range were relatively equal. Altitudes of the points were extracted from ASTER GDEM V2 with the resolution of 30 m. Structural equation modeling (SEM) integrates factor analysis and path analysis25. It has been widely applied to ecological and environmental studies19. Te model was constructed by IBM SPSS AMOS 21 sofware. Te model of this study was established by the potential relationships between the fve possible variables, i.e., area of Qinghai Lake, temperature, precipitation, evaporation, and snow line. Te data of precipitation, evaporation, and temperature were collected from Gangcha meteorological station, which is the nearest meteorological station to Qinghai Lake. Te snow line data was collected from Gangshika peak. Ten, the stochastic relationship between variables was tested by model reasonableness test.

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20. Gao, Y. H., Li, X., Leung, L. R., Chen, D. L. & Xu, J. W. Aridity changes in the Tibetan Plateau in a warming climate. Environ. Res. Lett. 10, 34013–34024 (2015). 21. Liu, X. D. & Chen, B. D. Climatic warming in the Tibetan Plateau during recent decades. Int. J. Clim. 20, 1729–1742 (2000). 22. You, Q. L. et al. Climate warming and associated changes in atmospheric circulation in the eastern and central Tibetan Plateau from a homogenized dataset. Global Planet Change 72, 11–24 (2010). 23. Feng, S., Tang, M. & Zhou, G. Water level changes of Qinghai Lake in recent 600 years. Lake Sci. 12, 205–210 (2000). 24. Xu, H. Q. Modifcation of normalised diference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006). 25. Wu, M. L. Structural equation modeling: Operation and application of AMOS. (Chongqing University Press, Chongqing, 2010). Acknowledgements Tis work was partially supported by Natural Science Foundation of Jiangsu Province of China (No. BK20150683), China Postdoctoral Science Foundation (No. 2017M610330), and the Fundamental Research Funds for the Central Universities (No. KYTZ201404 & KYZ201712). We thank the supports from SRT program (1613A05) at NJAU and Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (PPZY2015A061). We thank the data support from China Meteorological Network, USGS, and RS Cloud Mart. We also thank the data support from National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China. Author Contributions Z.L., Y.M., Y.Z. and S.H. conceived and organized the research study; L.T., X.D. and F.K. were responsible for processing Remote Sensing images and data calculation; F.Z. and Y.Z. collected and analyzed meteorological data; X.D. and Y.Z. established and interpreted the SEM model. All authors contributed to the research design, interpreting results and writing the paper. Additional Information Competing Interests: Te authors declare no competing interests. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre- ative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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